Iterative learning-based formation control for multiple quadrotor unmanned aerial vehicles

Zhao, Zhihui; Jing, Wang; Chen, YangQuan; Ju, Shuang

doi:10.1177/1729881420911520

Cited by 19 publications

(9 citation statements)

References 25 publications

(29 reference statements)

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“…ILCA is a powerful method for approximating the uncertainties of nonlinear dynamics [37]. e mentioned learning algorithm is proposed to solve the repeated tasks in each iteration, and it runs until the errors of states are zero [38]. ILCA is a more accurate method in comparison with other approximation methods because it is an online method that the next unknown control input is calculated based on recorded experiments in the previous iterations.…”

Section: Actuator Faults Estimation Based On Ilcamentioning

confidence: 99%

Fault-Tolerant Control of Quadrotor UAVs Based on Back-Stepping Integral Sliding Mode Approach and Iterative Learning Algorithm

Allahverdy

Fakharian

Menhaj

2021

Mathematical Problems in Engineering

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In this paper, a fault-tolerant control system based on back-stepping integral sliding mode controller (BISMC) is designed and analyzed for both nonlinear translational and rotational subsystems of the quadrotor unmanned aerial vehicles (UAVs). The novelty of this paper is about combination of a classic controller with a repetitive algorithm to reduce the response time to actuator faults and have better tracking performance. The actuator fault is defined based on the loss of effectiveness and bias fault. Next, the iterative learning control algorithm (ILCA) is used to compensate for the unknown fault input according to previous recorded experiences. In the normal condition (without actuators fault), BISMC can force the actual trajectories toward the desired commands and reduce chattering about control signals, and in the presence of the actuators fault or external disturbances, the mentioned learning algorithm can incline the accuracy of the tracking performance and compensate for the occurred error. The Lyapunov theory illustrates that the proposed control strategy can stabilize the system despite the actuators’ fault and external disturbances. The simulation results show the effectiveness of the proposed scheme in comparison with another method.

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Section: Actuator Faults Estimation Based On Ilcamentioning

confidence: 99%

Fault-Tolerant Control of Quadrotor UAVs Based on Back-Stepping Integral Sliding Mode Approach and Iterative Learning Algorithm

Allahverdy

Fakharian

Menhaj

2021

Mathematical Problems in Engineering

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“…In recent years, along with the improvement and development of sensor technology and wireless communication technology and intelligent control technology, the UAV has been widely used in military and civilian fields and has achieved remarkable results [1,2]. Compared with a single UAV system, a multi-UAV system can effectively overcome the shortcomings of the single UAV system and improve the overall performance of the system under the reasonable utilization of resources.…”

Section: Introductionmentioning

confidence: 99%

Artificial Potential Field-Based Multi-UAV Formation Control and Target Tracking

Song

Jiang

et al. 2022

International Journal of Aerospace Engineering

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To simultaneously achieve space formation flight and target tracking of multiple unmanned aerial vehicles (UAVs) and solve the rotation buffeting problem of the UAV, a robust formation control and target tracking algorithm is proposed. The artificial potential function consisting of formation control term and target tracking term is established, and its convergence is proved. The sliding mode control method with the saturation function is established, and a sufficient condition for sliding mode to occur is analysed. Finally, the numerical simulation is conducted for the proposed algorithm, and the simulation result is analysed. The results show that the proposed algorithm can quickly achieve the formation flight and target tracking of multi-UAVs and improve the tracking performance; meanwhile, it can effectively weaken the rotation buffeting and improve the robustness.

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“…Jin ( 2019 ) achieved stable tracking of followers to the leader with iteration learning method, where the only angle of sight observation is needed. Zhao et al ( 2020 ) considered the relative distance constraints between agents and planned collision avoidance trajectory by iteration learning. Sanz et al ( 2008 ) took the first step to apply the reinforcement learning method in the formation control.…”

Section: Introductionmentioning

confidence: 99%

A Distributed Multi-Agent Formation Control Method Based on Deep Q Learning

Xie

Chen³

2022

Front. Neurorobot.

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Distributed control method plays an important role in the formation of a multi-agent system (MAS), which is the prerequisite for an MAS to complete its missions. However, the lack of considering the collision risk between agents makes many distributed formation control methods lose practicability. In this article, a distributed formation control method that takes collision avoidance into account is proposed. At first, the MAS formation control problem can be divided into pair-wise unit formation problems where each agent moves to the expected position and only needs to avoid one obstacle. Then, a deep Q network (DQN) is applied to model the agent's unit controller for this pair-wise unit formation. The DQN controller is trained by using reshaped reward function and prioritized experience replay. The agents in MAS formation share the same unit DQN controller but get different commands due to various observations. Finally, through the min-max fusion of value functions of the DQN controller, the agent can always respond to the most dangerous avoidance. In this way, we get an easy-to-train multi-agent collision avoidance formation control method. In the end, unit formation simulation and multi-agent formation simulation results are presented to verify our method.

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Iterative learning-based formation control for multiple quadrotor unmanned aerial vehicles

Cited by 19 publications

References 25 publications

Fault-Tolerant Control of Quadrotor UAVs Based on Back-Stepping Integral Sliding Mode Approach and Iterative Learning Algorithm

Fault-Tolerant Control of Quadrotor UAVs Based on Back-Stepping Integral Sliding Mode Approach and Iterative Learning Algorithm

Artificial Potential Field-Based Multi-UAV Formation Control and Target Tracking

A Distributed Multi-Agent Formation Control Method Based on Deep Q Learning

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